Dataset Information

Association between antibiotic resistance and increasing ambient temperature in China: An ecological study with nationwide panel data.

ABSTRACT:

Background

Antibiotic resistance leads to longer hospital stays, higher medical costs, and increased mortality. However, research into the relationship between climate change and antibiotic resistance remains inconclusive. This study aims to address the gap in the literature by exploring the association of antibiotic resistance with regional ambient temperature and its changes over time.

Methods

Data were obtained from the China Antimicrobial Surveillance Network (CHINET), monitoring the prevalence of carbapenem-resistant Acinetobacter baumannii (CRAB), Klebsiella pneumoniae (CRKP) and Pseudomonas aeruginosa (CRPA) in 28 provinces/regions over the period from 2005 to 2019. Log-linear regression models were established to determine the association between ambient temperature and antibiotic resistance after adjustment for variations in socioeconomic, health service, and environmental factors.

Findings

A 1 °C increase in average ambient temperature was associated with 1.14-fold increase (95%-CI [1.07-1.23]) in CRKP prevalence and 1.06-fold increase (95%-CI [1.03-1.08]) in CRPA prevalence. There was an accumulative effect of year-by-year changes in ambient temperature, with the four-year sum showing the greatest effect on antibiotic resistance. Higher prevalence of antibiotic resistance was also associated with higher antibiotic consumption, lower density of health facilities, higher density of hospital beds and higher level of corruption.

Interpretation

Higher prevalence of antibiotic resistance is associated with increased regional ambient temperature. The development of antibiotic resistance under rising ambient temperature differs across various strains of bacteria.

Funding

The National Key R&D Program of China (grant number: 2018YFA0606200), National Natural Science Foundation of China (grant number: 72074234), Fundamental Scientific Research Funds for Central Universities, P.R. China (grant number: 22qntd4201), China Medical Board (grant number: CMB-OC-19-337).

SUBMITTER: Li W

PROVIDER: S-EPMC9672962 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association between antibiotic resistance and increasing ambient temperature in China: An ecological study with nationwide panel data.

Li Weibin W Liu Chaojie C Ho Hung Chak HC Shi Lin L Zeng Yingchao Y Yang Xinyi X Huang Qixian Q Pei Yi Y Huang Cunrui C Yang Lianping L

The Lancet regional health. Western Pacific 20221114

<h4>Background</h4>Antibiotic resistance leads to longer hospital stays, higher medical costs, and increased mortality. However, research into the relationship between climate change and antibiotic resistance remains inconclusive. This study aims to address the gap in the literature by exploring the association of antibiotic resistance with regional ambient temperature and its changes over time.<h4>Methods</h4>Data were obtained from the China Antimicrobial Surveillance Network (CHINET), monitor ...[more]

PMID: 36406382

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Project description:BackgroundRecent research has suggested that an increase in temperature can negatively affect mental health and increase hospitalization for mental illness. It is not clear, however, what factors or mechanisms mediate this association. We aimed to (1) investigate the associations between ambient temperatures and bad daily mood, and (2) identify variables affecting the strength of these associations (modifiers) including the time, the day of the week and the year of the mood rating, socio-demographic characteristics, sleep quality, psychiatric disorders and the personality trait neuroticism in the community.MethodsData stemmed from the second follow-up evaluation of CoLaus|PsyCoLaus, a prospective cohort study conducted in the general population of Lausanne (Switzerland). The 906 participants rated their mood level four times a day during seven days using a cell phone app. Mixed-effects logistic regression was used to determine the association between daily maximum temperature and mood level. Participant ID was inserted as a random effect in the model, whereas the time of the day, the day of the week and the year were inserted as fixed effects. Models were controlled for several confounders (socio-demographic characteristics, sleep quality, weather parameters and air pollutants). Stratified analyses were conducted based on socio-demographic characteristics, sleep quality, presence of psychiatric disorders or a high neuroticism.ResultsOverall, the probability of having a bad mood for the entire day decreased by 7.0% (OR: 0.93: 95% CI 0.88, 0.99) for each 5 °C increase in maximum temperature. A smaller and less precise effect (-3%; OR: 0.97: 95% CI 0.91, 1.03) was found when controlling for sunshine duration. A higher association was found in participants with bipolar disorder (-23%; OR: 0.77: 95% CI 0.51, 1.17) and in participants with a high neuroticism (-13%; OR: 0.87 95% CI 0.80, 0.95), whereas the association was reversed for participants with anxiety (20%; OR: 1.20: 95% CI 0.90, 1.59), depression (18%; OR: 1.18 95% CI 0.94, 1.48) and schizophrenia (193%; OR: 2.93 95% CI 1.17, 7.73).ConclusionsAccording to our findings, rising temperatures may positively affect mood in the general population. However, individuals with certain psychiatric disorders, such as anxiety, depression, and schizophrenia, may exhibit altered responses to heat, which may explain their increased morbidity when exposed to high temperatures. This suggests that tailored public health policies are required to protect this vulnerable population.

Project description:The evidence for the effects of environmental factors on COVID-19 case fatality remains controversial, and it is crucial to understand the role of preventable environmental factors in driving COVID-19 fatality. We thus conducted a nationwide cohort study to estimate the effects of environmental factors (temperature, particulate matter [PM2.5, PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) on COVID-19 case fatality. A total of 71,808 confirmed COVID-19 cases were identified and followed up for their vital status through April 25, 2020. Exposures to ambient air pollution and temperature were estimated by linking the city- and county-level monitoring data to the residential community of each participant. For each participant, two windows were defined: the period from symptom onset to diagnosis (exposure window I) and the period from diagnosis date to date of death/recovery or end of the study period (exposure window II). Cox proportional hazards models were used to estimate the associations between these environmental factors and COVID-19 case fatality. COVID-19 case fatality increased in association with environmental factors for the two exposure windows. For example, each 10 μg/m3 increase in PM2.5, PM10, O3, and NO2 in window I was associated with a hazard ratio of 1.11 (95% CI 1.09, 1.13), 1.10 (95% CI 1.08, 1.13), 1.09 (95 CI 1.03, 1.14), and 1.27 (95% CI 1.19, 1.35) for COVID-19 fatality, respectively. A significant effect was also observed for low temperature, with a hazard ratio of 1.03 (95% CI 1.01, 1.04) for COVID-19 case fatality per 1°C decrease. Subgroup analysis indicated that these effects were stronger in the elderly, as well as in those with mild symptoms and living in Wuhan or Hubei. Overall, the sensitivity analyses also yielded consistent estimates. Short-term exposure to ambient air pollution and low temperature during the illness would play a nonnegligible part in causing case fatality due to COVID-19. Reduced exposures to high concentrations of PM2.5, PM10, O3, SO2, and NO2 and low temperature would help improve the prognosis and reduce public health burden.

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Dataset Information

Association between antibiotic resistance and increasing ambient temperature in China: An ecological study with nationwide panel data.

Background

Methods

Findings

Interpretation

Funding

Publications

Association between antibiotic resistance and increasing ambient temperature in China: An ecological study with nationwide panel data.

Similar Datasets

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